Abstract
Multiple diseases have a strong metabolic component, and metabolomics as a powerful phenotyping technology, in combination with orthogonal biological and clinical approaches, will undoubtedly play a determinant role in accelerating the understanding of mechanisms that underlie these complex diseases determined by a set of genetic, lifestyle, and environmental exposure factors. Here, we provide several examples of valuable findings from metabolomics-led studies in diabetes and obesity metabolism, neurodegenerative disorders, and cancer metabolism and offer a longer term vision toward personalized approach to medicine, from population-based studies to pharmacometabolomics.
Key words
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Wishart DS (2016) Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov 15(7):473–484. https://doi.org/10.1038/nrd.2016.32
Johnson CH, Ivanisevic J, Siuzdak G (2016) Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol 17(7):451–459. https://doi.org/10.1038/nrm.2016.25
Ivanisevic J, Siuzdak G (2015) The role of metabolomics in brain metabolism research. J Neuroimmune Pharmacol 10:391–395
Schmidt CW (2004) Metabolomics: what’s happening downstream of DNA. Environ Health Perspect 112(7):A410–A415
Zamboni N, Saghatelian A, Patti GJ (2015) Defining the metabolome: size, flux, and regulation. Mol Cell 58(4):699–706. https://doi.org/10.1016/j.molcel.2015.04.021
Patti GJ, Yanes O, Siuzdak G (2012) Innovation: metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol 13(4):263–269. https://doi.org/10.1038/nrm3314
Sperber H, Mathieu J, Wang Y, Ferreccio A, Hesson J, Xu Z, Fischer KA, Devi A, Detraux D, Gu H, Battle SL, Showalter M, Valensisi C, Bielas JH, Ericson NG, Margaretha L, Robitaille AM, Margineantu D, Fiehn O, Hockenbery D, Blau CA, Raftery D, Margolin AA, Hawkins RD, Moon RT, Ware CB, Ruohola-Baker H (2015) The metabolome regulates the epigenetic landscape during naive-to-primed human embryonic stem cell transition. Nat Cell Biol 17(12):1523–1535. https://doi.org/10.1038/ncb3264
Sabari BR, Zhang D, Allis CD, Zhao Y (2017) Metabolic regulation of gene expression through histone acylations. Nat Rev Mol Cell Biol 18(2):90–101. https://doi.org/10.1038/nrm.2016.140
Siroux V, Agier L, Slama R (2016) The exposome concept: a challenge and a potential driver for environmental health research. Eur Respir Rev 25(140):124–129. https://doi.org/10.1183/16000617.0034-2016
Pedersen HK, Gudmundsdottir V, Nielsen HB, Hyotylainen T, Nielsen T, Jensen BAH, Forslund K, Hildebrand F, Prifti E, Falony G, Le Chatelier E, Levenez F, Doré J, Mattila I, Plichta DR, Pöhö P, Hellgren LI, Arumugam M, Sunagawa S, Vieira-Silva S, Jørgensen T, Holm JB, Trošt K, MetaHIT Consortium, Kristiansen K, Brix S, Raes J, Wang J, Hansen T, Bork P, Brunak S, Oresic M, Ehrlich SD, Pedersen O (2016) Human gut microbes impact host serum metabolome and insulin sensitivity. Nature 535(7612):376–381
Bucci M (2016) Gut microbiome: branching into metabolic disease. Nat Chem Biol 12(9):657–657. https://doi.org/10.1038/nchembio.2164
Mayer EA, Knight R, Mazmanian SK, Cryan JF, Tillisch K (2014) Gut microbes and the brain: paradigm shift in neuroscience. J Neurosci 34(46):15490–15496. https://doi.org/10.1523/jneurosci.3299-14.2014
Mamas M, Dunn WB, Neyses L, Goodacre R (2011) The role of metabolites and metabolomics in clinically applicable biomarkers of disease. Arch Toxicol 85(1):5–17. https://doi.org/10.1007/s00204-010-0609-6
Weber RJM, Lawson TN, Salek RM, Ebbels TMD, Glen RC, Goodacre R, Griffin JL, Haug K, Koulman A, Moreno P, Ralser M, Steinbeck C, Dunn WB, Viant MR (2017) Computational tools and workflows in metabolomics: an international survey highlights the opportunity for harmonisation through galaxy. Metabolomics 13(2):12. https://doi.org/10.1007/s11306-016-1147-x
Benton HP, Ivanisevic J, Mahieu NG, Kurczy ME, Johnson CH, Franco L, Rinehart D, Valentine E, Gowda H, Ubhi BK, Tautenhahn R, Gieschen A, Fields MW, Patti GJ, Siuzdak G (2015) Autonomous metabolomics for rapid metabolite identification in global profiling. Anal Chem 87(2):884–891. https://doi.org/10.1021/ac5025649
Tautenhahn R, Cho K, Uritboonthai W, Zhu Z, Patti GJ, Siuzdak G (2012) An accelerated workflow for untargeted metabolomics using the METLIN database. Nat Biotechnol 30(9):826–828. https://doi.org/10.1038/nbt.2348
Johnson CH, Ivanisevic J, Benton HP, Siuzdak G (2015) Bioinformatics: the next frontier of metabolomics. Anal Chem 87(1):147–156. https://doi.org/10.1021/ac5040693
Ivanisevic J, Elias D, Deguchi H, Averell PM, Kurczy M, Johnson CH, Tautenhahn R, Zhu Z, Watrous J, Jain M (2015) Arteriovenous blood metabolomics: a readout of intra-tissue metabostasis. Sci Rep 5:12757
Beger RD, Dunn W, Schmidt MA, Gross SS, Kirwan JA, Cascante M, Brennan L, Wishart DS, Oresic M, Hankemeier T, Broadhurst DI, Lane AN, Suhre K, Kastenmüller G, Sumner SJ, Thiele I, Fiehn O, Kaddurah-Daouk R, for “Precision M, Pharmacometabolomics Task Group”-Metabolomics Society I (2016) Metabolomics enables precision medicine: “a white paper, community perspective”. Metabolomics 12(9):149. https://doi.org/10.1007/s11306-016-1094-6
Su LJ, Fiehn O, Maruvada P, Moore SC, O’Keefe SJ, Wishart DS, Zanetti KA (2014) The use of metabolomics in population-based research. Adv Nutr 5(6):785–788. https://doi.org/10.3945/an.114.006494
Psychogios N, Hau DD, Peng J, Guo AC, Mandal R, Bouatra S, Sinelnikov I, Krishnamurthy R, Eisner R, Gautam B, Young N, Xia J, Knox C, Dong E, Huang P, Hollander Z, Pedersen TL, Smith SR, Bamforth F, Greiner R, McManus B, Newman JW, Goodfriend T, Wishart DS (2011) The human serum metabolome. PLoS One 6(2):e16957. https://doi.org/10.1371/journal.pone.0016957
Yanes O, Tautenhahn R, Patti GJ, Siuzdak G (2011) Expanding coverage of the metabolome for global metabolite profiling. Anal Chem 83(6):2152–2161. https://doi.org/10.1021/ac102981k
Zhang A, Sun H, Yan G, Wang P, Han Y, Wang X (2014) Metabolomics in diagnosis and biomarker discovery of colorectal cancer. Cancer Lett 345(1):17–20. https://doi.org/10.1016/j.canlet.2013.11.011
Thomas A, Lenglet S, Chaurand P, Deglon J, Mangin P, Mach F, Steffens S, Wolfender JL, Staub C (2011) Mass spectrometry for the evaluation of cardiovascular diseases based on proteomics and lipidomics. Thromb Haemost 106(1):20–33. https://doi.org/10.1160/TH10-12-0812
Dunn WB, Bailey NJ, Johnson HE (2005) Measuring the metabolome: current analytical technologies. Analyst 130(5):606–625. https://doi.org/10.1039/b418288j
Dunn WB, Broadhurst D, Begley P, Zelena E, Francis-McIntyre S, Anderson N, Brown M, Knowles JD, Halsall A, Haselden JN, Nicholls AW, Wilson ID, Kell DB, Goodacre R, Human Serum Metabolome Consortium (2011) Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc 6(7):1060–1083. https://doi.org/10.1038/nprot.2011.335
Schweiger R, Baier MC, Persicke M, Muller C (2014) High specificity in plant leaf metabolic responses to arbuscular mycorrhiza. Nat Commun 5:3886. https://doi.org/10.1038/ncomms4886
Tugizimana F, Steenkamp PA, Piater LA, Dubery IA (2014) Multi-platform metabolomic analyses of ergosterol-induced dynamic changes in Nicotiana tabacum cells. PLoS One 9(1):e87846. https://doi.org/10.1371/journal.pone.0087846
Bouatra S, Aziat F, Mandal R, Guo AC, Wilson MR, Knox C, Bjorndahl TC, Krishnamurthy R, Saleem F, Liu P, Dame ZT, Poelzer J, Huynh J, Yallou FS, Psychogios N, Dong E, Bogumil R, Roehring C, Wishart DS (2013) The human urine metabolome. PLoS One 8(9):e73076. https://doi.org/10.1371/journal.pone.0073076
Want EJ, Masson P, Michopoulos F, Wilson ID, Theodoridis G, Plumb RS, Shockcor J, Loftus N, Holmes E, Nicholson JK (2013) Global metabolic profiling of animal and human tissues via UPLC-MS. Nat Protoc 8(1):17–32. https://doi.org/10.1038/nprot.2012.135
Junot C, Fenaille F, Colsch B, Becher F (2013) High resolution mass spectrometry based techniques at the crossroads of metabolic pathways. Mass Spectrom Rev 33(6):471–500. https://doi.org/10.1002/mas.21401
Fiehn O (2002) Metabolomics – the link between genotypes and phenotypes. Plant Mol Biol 48(1–2):155–171
Wolfender JL, Marti G, Thomas A, Bertrand S (2014) Current approaches and challenges for the metabolite profiling of complex natural extracts. J Chromatogr A 1382:136–164. https://doi.org/10.1016/j.chroma.2014.10.091
Thomas A, Deglon J, Steimer T, Mangin P, Daali Y, Staub C (2010) On-line desorption of dried blood spots coupled to hydrophilic interaction/reversed-phase LC/MS/MS system for the simultaneous analysis of drugs and their polar metabolites. J Sep Sci 33(6–7):873–879. https://doi.org/10.1002/jssc.200900593
McCalley DV (2007) Is hydrophilic interaction chromatography with silica columns a viable alternative to reversed-phase liquid chromatography for the analysis of ionisable compounds? J Chromatogr A 1171(1–2):46–55. https://doi.org/10.1016/j.chroma.2007.09.047
Ivanisevic J, Zhu ZJ, Plate L, Tautenhahn R, Chen S, O’Brien PJ, Johnson CH, Marletta MA, Patti GJ, Siuzdak G (2013) Toward ‘omic scale metabolite profiling: a dual separation-mass spectrometry approach for coverage of lipid and central carbon metabolism. Anal Chem 85(14):6876–6884. https://doi.org/10.1021/ac401140h
Tautenhahn R, Patti GJ, Rinehart D, Siuzdak G (2012) XCMS Online: a web-based platform to process untargeted metabolomic data. Anal Chem 84(11):5035–5039. https://doi.org/10.1021/ac300698c
Xia J, Wishart DS (2011) Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst. Nat Protoc 6(6):743–760. https://doi.org/10.1038/nprot.2011.319
Eugster PJ, Glauser G, Wolfender JL (2013) Strategies in biomarker discovery. Peak annotation by MS and targeted LC-MS micro-fractionation for de novo structure identification by micro-NMR. Methods Mol Biol 1055:267–289. https://doi.org/10.1007/978-1-62703-577-4_19
Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, Djoumbou Y, Mandal R, Aziat F, Dong E, Bouatra S, Sinelnikov I, Arndt D, Xia J, Liu P, Yallou F, Bjorndahl T, Perez-Pineiro R, Eisner R, Allen F, Neveu V, Greiner R, Scalbert A (2013) HMDB 3.0—The Human Metabolome Database in 2013. Nucleic Acids Res 41(Database issue):D801–D807. https://doi.org/10.1093/nar/gks1065
Yuan M, Breitkopf SB, Yang X, Asara JM (2012) A positive/negative ion-switching, targeted mass spectrometry-based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue. Nat Protoc 7(5):872–881. https://doi.org/10.1038/nprot.2012.024
Cai Y, Weng K, Guo Y, Peng J, Zhu Z-J (2015) An integrated targeted metabolomic platform for high-throughput metabolite profiling and automated data processing. Metabolomics 11(6):1575–1586. https://doi.org/10.1007/s11306-015-0809-4
Norris JL, Caprioli RM (2013) Analysis of tissue specimens by matrix-assisted laser desorption/ionization imaging mass spectrometry in biological and clinical research. Chem Rev 113(4):2309–2342. https://doi.org/10.1021/cr3004295
Sun N, Ly A, Meding S, Witting M, Hauck SM, Ueffing M, Schmitt-Kopplin P, Aichler M, Walch A (2014) High-resolution metabolite imaging of light and dark treated retina using MALDI-FTICR mass spectrometry. Proteomics 14(7-8):913–923. https://doi.org/10.1002/pmic.201300407
Thomas A, Charbonneau JL, Fournaise E, Chaurand P (2012) Sublimation of new matrix candidates for high spatial resolution imaging mass spectrometry of lipids: Enhanced information in both positive and negative polarities after 1,5-diaminonapthalene deposition. Anal Chem 84(4):2048–2054. https://doi.org/10.1021/ac2033547
Ly A, Buck A, Balluff B, Sun N, Gorzolka K, Feuchtinger A, Janssen KP, Kuppen PJ, van de Velde CJ, Weirich G, Erlmeier F, Langer R, Aubele M, Zitzelsberger H, McDonnell L, Aichler M, Walch A (2016) High-mass-resolution MALDI mass spectrometry imaging of metabolites from formalin-fixed paraffin-embedded tissue. Nat Protoc 11(8):1428–1443. https://doi.org/10.1038/nprot.2016.081
Patterson NH, Alabdulkarim B, Lazaris A, Thomas A, Marcinkiewicz MM, Gao ZH, Vermeulen PB, Chaurand P, Metrakos P (2016) Assessment of pathological response to therapy using lipid mass spectrometry imaging. Sci Rep 6:36814. https://doi.org/10.1038/srep36814
Aichler M, Walch A (2015) MALDI Imaging mass spectrometry: current frontiers and perspectives in pathology research and practice. Lab Invest 95(4):422–431. https://doi.org/10.1038/labinvest.2014.156
Hamilton LK, Dufresne M, Joppe SE, Petryszyn S, Aumont A, Calon F, Barnabe-Heider F, Furtos A, Parent M, Chaurand P, Fernandes KJ (2015) Aberrant lipid metabolism in the forebrain niche suppresses adult neural stem cell proliferation in an animal model of Alzheimer’s disease. Cell Stem Cell 17(4):397–411. https://doi.org/10.1016/j.stem.2015.08.001
Tonnies E, Trushina E (2017) Oxidative stress, synaptic dysfunction, and Alzheimer’s disease. J Alzheimer’s Dis 57(4):1105–1121. https://doi.org/10.3233/jad-161088
Stranahan AM, Mattson MP (2012) Recruiting adaptive cellular stress responses for successful brain ageing. Nat Rev Neurosci 13(3):209–216. https://doi.org/10.1038/nrn3151
Jiao H, Arner P, Hoffstedt J, Brodin D, Dubern B, Czernichow S, van’t Hooft F, Axelsson T, Pedersen O, Hansen T, Sorensen TI, Hebebrand J, Kere J, Dahlman-Wright K, Hamsten A, Clement K, Dahlman I (2011) Genome wide association study identifies KCNMA1 contributing to human obesity. BMC Med Genomics 4:51. https://doi.org/10.1186/1755-8794-4-51
Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, Powell C, Vedantam S, Buchkovich ML, Yang J, Croteau-Chonka DC, Esko T, Fall T, Ferreira T, Gustafsson S, Kutalik Z, Luan J, Mägi R, Randall JC, Winkler TW, Wood AR, Workalemahu T, Faul JD, Smith JA, Zhao JH, Zhao W, Chen J, Fehrmann R, Hedman ÅK, Karjalainen J, Schmidt EM, Absher D, Amin N, Anderson D, Beekman M, Bolton JL, Bragg-Gresham JL, Buyske S, Demirkan A, Deng G, Ehret GB, Feenstra B, Feitosa MF, Fischer K, Goel A, Gong J, Jackson AU, Kanoni S, Kleber ME, Kristiansson K, Lim U, Lotay V, Mangino M, Leach IM, Medina-Gomez C, Medland SE, Nalls MA, Palmer CD, Pasko D, Pechlivanis S, Peters MJ, Prokopenko I, Shungin D, Stančáková A, Strawbridge RJ, Sung YJ, Tanaka T, Teumer A, Trompet S, van der Laan SW, van Setten J, Van Vliet-Ostaptchouk JV, Wang Z, Yengo L, Zhang W, Isaacs A, Albrecht E, Ärnlöv J, Arscott GM, Attwood AP, Bandinelli S, Barrett A, Bas IN, Bellis C, Bennett AJ, Berne C, Blagieva R, Blüher M, Böhringer S, Bonnycastle LL, Böttcher Y, Boyd HA, Bruinenberg M, Caspersen IH, Chen YI, Clarke R, Daw EW, de Craen AJM, Delgado G, Dimitriou M, Doney ASF, Eklund N, Estrada K, Eury E, Folkersen L, Fraser RM, Garcia ME, Geller F, Giedraitis V, Gigante B, Go AS, Golay A, Goodall AH, Gordon SD, Gorski M, Grabe HJ, Grallert H, Grammer TB, Gräßler J, Grönberg H, Groves CJ, Gusto G, Haessler J, Hall P, Haller T, Hallmans G, Hartman CA, Hassinen M, Hayward C, Heard-Costa NL, Helmer Q, Hengstenberg C, Holmen O, Hottenga JJ, James AL, Jeff JM, Johansson Å, Jolley J, Juliusdottir T, Kinnunen L, Koenig W, Koskenvuo M, Kratzer W, Laitinen J, Lamina C, Leander K, Lee NR, Lichtner P, Lind L, Lindström J, Lo KS, Lobbens S, Lorbeer R, Lu Y, Mach F, Magnusson PKE, Mahajan A, McArdle WL, McLachlan S, Menni C, Merger S, Mihailov E, Milani L, Moayyeri A, Monda KL, Morken MA, Mulas A, Müller G, Müller-Nurasyid M, Musk AW, Nagaraja R, Nöthen MM, Nolte IM, Pilz S, Rayner NW, Renstrom F, Rettig R, Ried JS, Ripke S, Robertson NR, Rose LM, Sanna S, Scharnagl H, Scholtens S, Schumacher FR, Scott WR, Seufferlein T, Shi J, Smith AV, Smolonska J, Stanton AV, Steinthorsdottir V, Stirrups K, Stringham HM, Sundström J, Swertz MA, Swift AJ, Syvänen AC, Tan ST, Tayo BO, Thorand B, Thorleifsson G, Tyrer JP, Uh HW, Vandenput L, Verhulst FC, Vermeulen SH, Verweij N, Vonk JM, Waite LL, Warren HR, Waterworth D, Weedon MN, Wilkens LR, Willenborg C, Wilsgaard T, Wojczynski MK, Wong A, Wright AF, Zhang Q, LifeLines Cohort Study, Brennan EP, Choi M, Dastani Z, Drong AW, Eriksson P, Franco-Cereceda A, Gådin JR, Gharavi AG, Goddard ME, Handsaker RE, Huang J, Karpe F, Kathiresan S, Keildson S, Kiryluk K, Kubo M, Lee JY, Liang L, Lifton RP, Ma B, McCarroll SA, McKnight AJ, Min JL, Moffatt MF, Montgomery GW, Murabito JM, Nicholson G, Nyholt DR, Okada Y, JRB P, Dorajoo R, Reinmaa E, Salem RM, Sandholm N, Scott RA, Stolk L, Takahashi A, Tanaka T, van’t Hooft FM, AAE V, Westra HJ, Zheng W, Zondervan KT, ADIPOGen Consortium, AGEN-BMI Working Group, CARDIOGRAMplusC4D Consortium, CKDGen Consortium, GLGC, ICBP, MAGIC Investigators, MuTHER Consortium, MIGen Consortium, PAGE Consortium, ReproGen Consortium, GENIE Consortium, International Endogene Consortium, Heath AC, Arveiler D, SJL B, Beilby J, Bergman RN, Blangero J, Bovet P, Campbell H, Caulfield MJ, Cesana G, Chakravarti A, Chasman DI, Chines PS, Collins FS, Crawford DC, Cupples LA, Cusi D, Danesh J, de Faire U, den Ruijter HM, Dominiczak AF, Erbel R, Erdmann J, Eriksson JG, Farrall M, Felix SB, Ferrannini E, Ferrières J, Ford I, Forouhi NG, Forrester T, Franco OH, Gansevoort RT, Gejman PV, Gieger C, Gottesman O, Gudnason V, Gyllensten U, Hall AS, Harris TB, Hattersley AT, Hicks AA, Hindorff LA, Hingorani AD, Hofman A, Homuth G, Hovingh GK, Humphries SE, Hunt SC, Hyppönen E, Illig T, Jacobs KB, Jarvelin MR, Jöckel KH, Johansen B, Jousilahti P, Jukema JW, Jula AM, Kaprio J, Kastelein JJP, Keinanen-Kiukaanniemi SM, Kiemeney LA, Knekt P, Kooner JS, Kooperberg C, Kovacs P, Kraja AT, Kumari M, Kuusisto J, Lakka TA, Langenberg C, Marchand LL, Lehtimäki T, Lyssenko V, Männistö S, Marette A, Matise TC, McKenzie CA, McKnight B, Moll FL, Morris AD, Morris AP, Murray JC, Nelis M, Ohlsson C, Oldehinkel AJ, Ong KK, PAF M, Pasterkamp G, Peden JF, Peters A, Postma DS, Pramstaller PP, Price JF, Qi L, Raitakari OT, Rankinen T, Rao DC, Rice TK, Ridker PM, Rioux JD, Ritchie MD, Rudan I, Salomaa V, Samani NJ, Saramies J, Sarzynski MA, Schunkert H, Schwarz PEH, Sever P, Shuldiner AR, Sinisalo J, Stolk RP, Strauch K, Tönjes A, Trégouët DA, Tremblay A, Tremoli E, Virtamo J, Vohl MC, Völker U, Waeber G, Willemsen G, Witteman JC, Zillikens MC, Adair LS, Amouyel P, Asselbergs FW, Assimes TL, Bochud M, Boehm BO, Boerwinkle E, Bornstein SR, Bottinger EP, Bouchard C, Cauchi S, Chambers JC, Chanock SJ, Cooper RS, de Bakker PIW, Dedoussis G, Ferrucci L, Franks PW, Froguel P, Groop LC, Haiman CA, Hamsten A, Hui J, Hunter DJ, Hveem K, Kaplan RC, Kivimaki M, Kuh D, Laakso M, Liu Y, Martin NG, März W, Melbye M, Metspalu A, Moebus S, Munroe PB, Njølstad I, Oostra BA, Palmer CNA, Pedersen NL, Perola M, Pérusse L, Peters U, Power C, Quertermous T, Rauramaa R, Rivadeneira F, Saaristo TE, Saleheen D, Sattar N, Schadt EE, Schlessinger D, Slagboom PE, Snieder H, Spector TD, Thorsteinsdottir U, Stumvoll M, Tuomilehto J, Uitterlinden AG, Uusitupa M, van der Harst P, Walker M, Wallaschofski H, Wareham NJ, Watkins H, Weir DR, Wichmann HE, Wilson JF, Zanen P, Borecki IB, Deloukas P, Fox CS, Heid IM, O’Connell JR, Strachan DP, Stefansson K, van Duijn CM, Abecasis GR, Franke L, Frayling TM, McCarthy MI, Visscher PM, Scherag A, Willer CJ, Boehnke M, Mohlke KL, Lindgren CM, Beckmann JS, Barroso I, North KE, Ingelsson E, Hirschhorn JN, Loos RJF, Speliotes EK (2015) Genetic studies of body mass index yield new insights for obesity biology. Nature 518(7538):197–206. https://doi.org/10.1038/nature14177
Moreno-Navarrete JM, Jove M, Ortega F, Xifra G, Ricart W, Obis E, Pamplona R, Portero-Otin M, Fernandez-Real JM (2016) Metabolomics uncovers the role of adipose tissue PDXK in adipogenesis and systemic insulin sensitivity. Diabetologia 59(4):822–832. https://doi.org/10.1007/s00125-016-3863-1
Abu Bakar MH, Sarmidi MR, Cheng KK, Ali Khan A, Suan CL, Zaman Huri H, Yaakob H (2015) Metabolomics – the complementary field in systems biology: a review on obesity and type 2 diabetes. Mol Biosyst 11(7):1742–1774. https://doi.org/10.1039/c5mb00158g
Roberts LD, Koulman A, Griffin JL (2014) Towards metabolic biomarkers of insulin resistance and type 2 diabetes: progress from the metabolome. Lancet Diabetes Endocrinol 2(1):65–75. https://doi.org/10.1016/S2213-8587(13)70143-8
Morris C, O’Grada C, Ryan M, Roche HM, Gibney MJ, Gibney ER, Brennan L (2012) The relationship between BMI and metabolomic profiles: a focus on amino acids. Proc Nutr Soc 71(4):634–638. https://doi.org/10.1017/S0029665112000699
Newgard CB, An J, Bain JR, Muehlbauer MJ, Stevens RD, Lien LF, Haqq AM, Shah SH, Arlotto M, Slentz CA, Rochon J, Gallup D, Ilkayeva O, Wenner BR, Yancy WS Jr, Eisenson H, Musante G, Surwit RS, Millington DS, Butler MD, Svetkey LP (2009) A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab 9(4):311–326. https://doi.org/10.1016/j.cmet.2009.02.002
Makki K, Froguel P, Wolowczuk I (2013) Adipose tissue in obesity-related inflammation and insulin resistance: cells, cytokines, and chemokines. ISRN Inflamm 2013:139239. https://doi.org/10.1155/2013/139239
Sun K, Kusminski CM, Scherer PE (2011) Adipose tissue remodeling and obesity. J Clin Invest 121(6):2094–2101. https://doi.org/10.1172/JCI45887
La Merrill M, Emond C, Kim MJ, Antignac JP, Le Bizec B, Clement K, Birnbaum LS, Barouki R (2013) Toxicological function of adipose tissue: focus on persistent organic pollutants. Environ Health Perspect 121(2):162–169. https://doi.org/10.1289/ehp.1205485
Frayn KN, Karpe F, Fielding BA, Macdonald IA, Coppack SW (2003) Integrative physiology of human adipose tissue. Int J Obes Relat Metab Disord 27(8):875–888. https://doi.org/10.1038/sj.ijo.0802326
Hanzu FA, Vinaixa M, Papageorgiou A, Parrizas M, Correig X, Delgado S, Carmona F, Samino S, Vidal J, Gomis R (2014) Obesity rather than regional fat depots marks the metabolomic pattern of adipose tissue: an untargeted metabolomic approach. Obesity (Silver Spring) 22(3):698–704. https://doi.org/10.1002/oby.20541
Cao H, Gerhold K, Mayers JR, Wiest MM, Watkins SM, Hotamisligil GS (2008) Identification of a lipokine, a lipid hormone linking adipose tissue to systemic metabolism. Cell 134(6):933–944. https://doi.org/10.1016/j.cell.2008.07.048
Liesenfeld DB, Grapov D, Fahrmann JF, Salou M, Scherer D, Toth R, Habermann N, Bohm J, Schrotz-King P, Gigic B, Schneider M, Ulrich A, Herpel E, Schirmacher P, Fiehn O, Lampe JW, Ulrich CM (2015) Metabolomics and transcriptomics identify pathway differences between visceral and subcutaneous adipose tissue in colorectal cancer patients: the ColoCare study. Am J Clin Nutr 102(2):433–443. https://doi.org/10.3945/ajcn.114.103804
Perez-Cornago A, Brennan L, Ibero-Baraibar I, Hermsdorff HH, O’Gorman A, Zulet MA, Martinez JA (2014) Metabolomics identifies changes in fatty acid and amino acid profiles in serum of overweight older adults following a weight loss intervention. J Physiol Biochem 70(2):593–602. https://doi.org/10.1007/s13105-013-0311-2
Shah SH, Crosslin DR, Haynes CS, Nelson S, Turer CB, Stevens RD, Muehlbauer MJ, Wenner BR, Bain JR, Laferrere B, Gorroochurn P, Teixeira J, Brantley PJ, Stevens VJ, Hollis JF, Appel LJ, Lien LF, Batch B, Newgard CB, Svetkey LP (2012) Branched-chain amino acid levels are associated with improvement in insulin resistance with weight loss. Diabetologia 55(2):321–330. https://doi.org/10.1007/s00125-011-2356-5
Sjostrom L (2013) Review of the key results from the Swedish Obese Subjects (SOS) trial – a prospective controlled intervention study of bariatric surgery. J Intern Med 273(3):219–234. https://doi.org/10.1111/joim.12012
Buchwald H, Avidor Y, Braunwald E, Jensen MD, Pories W, Fahrbach K, Schoelles K (2004) Bariatric surgery: a systematic review and meta-analysis. JAMA 292(14):1724–1737. https://doi.org/10.1001/jama.292.14.1724
Liu SY, Wong SK, Lam CC, Yung MY, Kong AP, Ng EK (2015) Long-term results on weight loss and diabetes remission after laparoscopic sleeve gastrectomy for a morbidly obese Chinese population. Obes Surg 25(10):1901–1908. https://doi.org/10.1007/s11695-015-1628-4
Parikh M, Pomp A, Gagner M (2007) Laparoscopic conversion of failed gastric bypass to duodenal switch: technical considerations and preliminary outcomes. Surg Obes Relat Dis 3(6):611–618. doi:S1550-7289(07)00569-2 [pii]. https://doi.org/10.1016/j.soard.2007.07.010
Mutch DM, Fuhrmann JC, Rein D, Wiemer JC, Bouillot JL, Poitou C, Clement K (2009) Metabolite profiling identifies candidate markers reflecting the clinical adaptations associated with Roux-en-Y gastric bypass surgery. PLoS One 4(11):e7905. https://doi.org/10.1371/journal.pone.0007905
Laferrere B, Reilly D, Arias S, Swerdlow N, Gorroochurn P, Bawa B, Bose M, Teixeira J, Stevens RD, Wenner BR, Bain JR, Muehlbauer MJ, Haqq A, Lien L, Shah SH, Svetkey LP, Newgard CB (2011) Differential metabolic impact of gastric bypass surgery versus dietary intervention in obese diabetic subjects despite identical weight loss. Sci Transl Med 3(80):80re82. https://doi.org/10.1126/scitranslmed.3002043
Whiting DR, Guariguata L, Weil C, Shaw J (2011) IDF diabetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030. Diabetes Res Clin Pract 94(3):311–321. https://doi.org/10.1016/j.diabres.2011.10.029
Huber CA, Schwenkglenks M, Rapold R, Reich O (2014) Epidemiology and costs of diabetes mellitus in Switzerland: an analysis of health care claims data, 2006 and 2011. BMC Endocr Disord 14:44. https://doi.org/10.1186/1472-6823-14-44
Schmid R, Vollenweider P, Waeber G, Marques-Vidal P (2011) Estimating the risk of developing type 2 diabetes: a comparison of several risk scores: the Cohorte Lausannoise study. Diabetes Care 34(8):1863–1868. https://doi.org/10.2337/dc11-0206
Marques-Vidal P, Schmid R, Bochud M, Bastardot F, von Kanel R, Paccaud F, Glaus J, Preisig M, Waeber G, Vollenweider P (2012) Adipocytokines, hepatic and inflammatory biomarkers and incidence of type 2 diabetes. the CoLaus study. PLoS One 7(12):e51768. https://doi.org/10.1371/journal.pone.0051768
Vaxillaire M, Yengo L, Lobbens S, Rocheleau G, Eury E, Lantieri O, Marre M, Balkau B, Bonnefond A, Froguel P (2014) Type 2 diabetes-related genetic risk scores associated with variations in fasting plasma glucose and development of impaired glucose homeostasis in the prospective DESIR study. Diabetologia 57(8):1601–1610. https://doi.org/10.1007/s00125-014-3277-x
Lin X, Song K, Lim N, Yuan X, Johnson T, Abderrahmani A, Vollenweider P, Stirnadel H, Sundseth SS, Lai E, Burns DK, Middleton LT, Roses AD, Matthews PM, Waeber G, Cardon L, Waterworth DM, Mooser V (2009) Risk prediction of prevalent diabetes in a Swiss population using a weighted genetic score – the CoLaus Study. Diabetologia 52(4):600–608. https://doi.org/10.1007/s00125-008-1254-y
Meigs JB, Shrader P, Sullivan LM, McAteer JB, Fox CS, Dupuis J, Manning AK, Florez JC, Wilson PW, D’Agostino RB Sr, Cupples LA (2008) Genotype score in addition to common risk factors for prediction of type 2 diabetes. N Engl J Med 359(21):2208–2219. https://doi.org/10.1056/NEJMoa0804742
Kussmann M, Morine MJ, Hager J, Sonderegger B, Kaput J (2013) Perspective: a systems approach to diabetes research. Front Genet 4:205. https://doi.org/10.3389/fgene.2013.00205
Klein MS, Shearer J (2016) Metabolomics and type 2 diabetes: translating basic research into clinical application. J Diabetes Res 2016:3898502. https://doi.org/10.1155/2016/3898502
Wang-Sattler R, Yu Z, Herder C, Messias AC, Floegel A, He Y, Heim K, Campillos M, Holzapfel C, Thorand B, Grallert H, Xu T, Bader E, Huth C, Mittelstrass K, Doring A, Meisinger C, Gieger C, Prehn C, Roemisch-Margl W, Carstensen M, Xie L, Yamanaka-Okumura H, Xing G, Ceglarek U, Thiery J, Giani G, Lickert H, Lin X, Li Y, Boeing H, Joost HG, de Angelis MH, Rathmann W, Suhre K, Prokisch H, Peters A, Meitinger T, Roden M, Wichmann HE, Pischon T, Adamski J, Illig T (2012) Novel biomarkers for pre-diabetes identified by metabolomics. Mol Syst Biol 8:615. https://doi.org/10.1038/msb.2012.43
Padberg I, Peter E, Gonzalez-Maldonado S, Witt H, Mueller M, Weis T, Bethan B, Liebenberg V, Wiemer J, Katus HA, Rein D, Schatz P (2014) A new metabolomic signature in type-2 diabetes mellitus and its pathophysiology. PLoS One 9(1):e85082. https://doi.org/10.1371/journal.pone.0085082
Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, Lewis GD, Fox CS, Jacques PF, Fernandez C, O’Donnell CJ, Carr SA, Mootha VK, Florez JC, Souza A, Melander O, Clish CB, Gerszten RE (2011) Metabolite profiles and the risk of developing diabetes. Nat Med 17(4):448–453. https://doi.org/10.1038/nm.2307
Newgard CB (2012) Interplay between lipids and branched-chain amino acids in development of insulin resistance. Cell Metab 15(5):606–614. https://doi.org/10.1016/j.cmet.2012.01.024
Herman MA, She P, Peroni OD, Lynch CJ, Kahn BB (2010) Adipose tissue branched chain amino acid (BCAA) metabolism modulates circulating BCAA levels. J Biol Chem 285(15):11348–11356. https://doi.org/10.1074/jbc.M109.075184
Sears DD, Hsiao G, Hsiao A, Yu JG, Courtney CH, Ofrecio JM, Chapman J, Subramaniam S (2009) Mechanisms of human insulin resistance and thiazolidinedione-mediated insulin sensitization. Proc Natl Acad Sci U S A 106(44):18745–18750. https://doi.org/10.1073/pnas.0903032106
Kaeberlein M, Rabinovitch PS, Martin GM (2015) Healthy aging: the ultimate preventative medicine. Science 350(6265):1191–1193. https://doi.org/10.1126/science.aad3267
Jove M, Portero-Otin M, Naudi A, Ferrer I, Pamplona R (2014) Metabolomics of human brain aging and age-related neurodegenerative diseases. J Neuropathol Exp Neurol 73(7):640–657. https://doi.org/10.1097/nen.0000000000000091
Magistretti PJ, Allaman I (2015) A cellular perspective on brain energy metabolism and functional imaging. Neuron 86(4):883–901. https://doi.org/10.1016/j.neuron.2015.03.035
Trushina E, Dutta T, Persson XM, Mielke MM, Petersen RC (2013) Identification of altered metabolic pathways in plasma and CSF in mild cognitive impairment and Alzheimer’s disease using metabolomics. PLoS One 8(5):e63644. https://doi.org/10.1371/journal.pone.0063644
Toledo JB, Arnold M, Kastenmüller G, Chang R, Baillie RA, Han X, Thambisetty M, Tenenbaum JD, Suhre K, Thompson JW, John-Williams LS, MahmoudianDehkordi S, Rotroff DM, Jack JR, Motsinger-Reif A, Risacher SL, Blach C, Lucas JE, Massaro T, Louie G, Zhu H, Dallmann G, Klavins K, Koal T, Kim S, Nho K, Shen L, Casanova R, Varma S, Legido-Quigley C, Moseley MA, Zhu K, Henrion MYR, van der Lee SJ, Harms AC, Demirkan A, Hankemeier T, van Duijn CM, Trojanowski JQ, Shaw LM, Saykin AJ, Weiner MW, Doraiswamy PM, Kaddurah-Daouk R (2017) Metabolic network failures in Alzheimer’s disease – a biochemical road map. Alzheimers Dement 13(9):965–984. https://doi.org/10.1016/j.jalz.2017.01.020
Mink JW, Blumenschine RJ, Adams DB (1981) Ratio of central nervous system to body metabolism in vertebrates: its constancy and functional basis. Am J Physiol 241(3):R203–R212
Paglia G, Stocchero M, Cacciatore S, Lai S, Angel P, Alam MT, Keller M, Ralser M, Astarita G (2016) Unbiased metabolomic investigation of Alzheimer’s disease brain points to dysregulation of mitochondrial aspartate metabolism. J Proteome Res 15(2):608–618. https://doi.org/10.1021/acs.jproteome.5b01020
Kapogiannis D, Mattson MP (2011) Disrupted energy metabolism and neuronal circuit dysfunction in cognitive impairment and Alzheimer’s disease. Lancet Neurol 10(2):187–198. https://doi.org/10.1016/s1474-4422(10)70277-5
Trushina E, Nemutlu E, Zhang S, Christensen T, Camp J, Mesa J, Siddiqui A, Tamura Y, Sesaki H, Wengenack TM, Dzeja PP, Poduslo JF (2012) Defects in mitochondrial dynamics and metabolomic signatures of evolving energetic stress in mouse models of familial Alzheimer’s disease. PLoS One 7(2):e32737. https://doi.org/10.1371/journal.pone.0032737
Han X, Rozen S, Boyle SH, Hellegers C, Cheng H, Burke JR, Welsh-Bohmer KA, Doraiswamy PM, Kaddurah-Daouk R (2011) Metabolomics in early Alzheimer’s disease: identification of altered plasma sphingolipidome using shotgun lipidomics. PLoS One 6(7):e21643. https://doi.org/10.1371/journal.pone.0021643
Mapstone M, Cheema AK, Fiandaca MS, Zhong X, Mhyre TR, MacArthur LH, Hall WJ, Fisher SG, Peterson DR, Haley JM, Nazar MD, Rich SA, Berlau DJ, Peltz CB, Tan MT, Kawas CH, Federoff HJ (2014) Plasma phospholipids identify antecedent memory impairment in older adults. Nat Med 20(4):415–418. https://doi.org/10.1038/nm.3466
Kang J, Lu J, Zhang X (2015) Metabolomics-based promising candidate biomarkers and pathways in Alzheimer’s disease. Pharmazie 70(5):277–282
Ansoleaga B, Jove M, Schluter A, Garcia-Esparcia P, Moreno J, Pujol A, Pamplona R, Portero-Otin M, Ferrer I (2015) Deregulation of purine metabolism in Alzheimer’s disease. Neurobiol Aging 36(1):68–80. https://doi.org/10.1016/j.neurobiolaging.2014.08.004
Mattson MP (1998) Modification of ion homeostasis by lipid peroxidation: roles in neuronal degeneration and adaptive plasticity. Trends Neurosci 21(2):53–57
Mattson MP, Gleichmann M, Cheng A (2008) Mitochondria in neuroplasticity and neurological disorders. Neuron 60(5):748–766. https://doi.org/10.1016/j.neuron.2008.10.010
Payne BAI, Chinnery PF (2015) Mitochondrial dysfunction in aging: Much progress but many unresolved questions. Biochimica et Biophysica Acta 1847(11):1347–1353
Mouchiroud L, Houtkooper RH, Moullan N, Katsyuba E, Ryu D, Cantó C, Mottis A, Jo Y-S, Viswanathan M, Schoonjans K, Guarente L, Auwerx J (2013) The NAD(+)/sirtuin pathway modulates longevity through activation of mitochondrial UPR and FOXO signaling. Cell 154(2):430–441. https://doi.org/10.1016/j.cell.2013.06.016
Ibanez C, Simo C, Martin-Alvarez PJ, Kivipelto M, Winblad B, Cedazo-Minguez A, Cifuentes A (2012) Toward a predictive model of Alzheimer’s disease progression using capillary electrophoresis-mass spectrometry metabolomics. Anal Chem 84(20):8532–8540. https://doi.org/10.1021/ac301243k
Graham SF, Chevallier OP, Roberts D, Holscher C, Elliott CT, Green BD (2013) Investigation of the human brain metabolome to identify potential markers for early diagnosis and therapeutic targets of Alzheimer’s disease. Anal Chem 85(3):1803–1811. https://doi.org/10.1021/ac303163f
Wishart DS (2015) Is cancer a genetic disease or a metabolic disease? EBioMedicine 2(6):478–479. https://doi.org/10.1016/j.ebiom.2015.05.022
Hanahan D, Weinberg RA Hallmarks of cancer: the next generation. Cell 144(5):646–674. https://doi.org/10.1016/j.cell.2011.02.013
Otto AM (2016) Warburg effect(s)—a biographical sketch of Otto Warburg and his impacts on tumor metabolism. Cancer Metab 4:5. https://doi.org/10.1186/s40170-016-0145-9
Yang M, Soga T, Pollard PJ (2013) Oncometabolites: linking altered metabolism with cancer. J Clin Invest 123(9):3652–3658. https://doi.org/10.1172/JCI67228
Morin A, Letouze E, Gimenez-Roqueplo AP, Favier J (2014) Oncometabolites-driven tumorigenesis: From genetics to targeted therapy. Int J Cancer 135(10):2237–2248. https://doi.org/10.1002/ijc.29080
Wang X, Yang K, Xie Q, Wu Q, Mack SC, Shi Y, Kim LJ, Prager BC, Flavahan WA, Liu X, Singer M, Hubert CG, Miller TE, Zhou W, Huang Z, Fang X, Regev A, Suva ML, Hwang TH, Locasale JW, Bao S, Rich JN (2017) Purine synthesis promotes maintenance of brain tumor initiating cells in glioma. Nat Neurosci 20(5):661–673. https://doi.org/10.1038/nn.4537
Chang CH, Pearce EL (2016) Emerging concepts of T cell metabolism as a target of immunotherapy. Nat Immunol 17(4):364–368. https://doi.org/10.1038/ni.3415
Shin SY, Fauman EB, Petersen AK, Krumsiek J, Santos R, Huang J, Arnold M, Erte I, Forgetta V, Yang TP, Walter K, Menni C, Chen L, Vasquez L, Valdes AM, Hyde CL, Wang V, Ziemek D, Roberts P, Xi L, Grundberg E, Multiple Tissue Human Expression Resource Consortium, Waldenberger M, Richards JB, Mohney RP, Milburn MV, John SL, Trimmer J, Theis FJ, Overington JP, Suhre K, Brosnan MJ, Gieger C, Kastenmuller G, Spector TD, Soranzo N (2014) An atlas of genetic influences on human blood metabolites. Nat Genet 46(6):543–550. https://doi.org/10.1038/ng.2982
Draisma HH, Pool R, Kobl M, Jansen R, Petersen AK, Vaarhorst AA, Yet I, Haller T, Demirkan A, Esko T, Zhu G, Bohringer S, Beekman M, van Klinken JB, Romisch-Margl W, Prehn C, Adamski J, de Craen AJ, van Leeuwen EM, Amin N, Dharuri H, Westra HJ, Franke L, de Geus EJ, Hottenga JJ, Willemsen G, Henders AK, Montgomery GW, Nyholt DR, Whitfield JB, Penninx BW, Spector TD, Metspalu A, Eline Slagboom P, van Dijk KW, t Hoen PA, Strauch K, Martin NG, van Ommen GJ, Illig T, Bell JT, Mangino M, Suhre K, McCarthy MI, Gieger C, Isaacs A, van Duijn CM, Boomsma DI (2015) Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels. Nat Commun 6:7208. https://doi.org/10.1038/ncomms8208
Suhre K, Shin SY, Petersen AK, Mohney RP, Meredith D, Wagele B, Altmaier E, CARDIoGRAM, Deloukas P, Erdmann J, Grundberg E, Hammond CJ, de Angelis MH, Kastenmuller G, Kottgen A, Kronenberg F, Mangino M, Meisinger C, Meitinger T, Mewes HW, Milburn MV, Prehn C, Raffler J, Ried JS, Romisch-Margl W, Samani NJ, Small KS, Wichmann HE, Zhai G, Illig T, Spector TD, Adamski J, Soranzo N, Gieger C (2011) Human metabolic individuality in biomedical and pharmaceutical research. Nature 477(7362):54–60. https://doi.org/10.1038/nature10354
Kaddurah-Daouk R, Weinshilboum R, Pharmacometabolomics Research Network (2015) Metabolomic signatures for drug response phenotypes: pharmacometabolomics enables precision medicine. Clin Pharmacol Ther 98(1):71–75. https://doi.org/10.1002/cpt.134
Everett JR (2016) From metabonomics to pharmacometabonomics: the role of metabolic profiling in personalized medicine. Front Pharmacol 7:297. https://doi.org/10.3389/fphar.2016.00297
Lewis JP, Yerges-Armstrong LM, Ellero-Simatos S, Georgiades A, Kaddurah-Daouk R, Hankemeier T (2013) Integration of pharmacometabolomic and pharmacogenomic approaches reveals novel insights into antiplatelet therapy. Clin Pharmacol Ther 94(5):570–573. https://doi.org/10.1038/clpt.2013.153
Neavin D, Kaddurah-Daouk R, Weinshilboum R (2016) Pharmacometabolomics informs Pharmacogenomics. Metabolomics 12(7). https://doi.org/10.1007/s11306-016-1066-x
Nicholson JK, Connelly J, Lindon JC, Holmes E (2002) Metabonomics: a platform for studying drug toxicity and gene function. Nat Rev Drug Discov 1(2):153–161. https://doi.org/10.1038/nrd728
Spear BB, Heath-Chiozzi M, Huff J (2001) Clinical application of pharmacogenetics. Trends Mol Med 7(5):201–204
Bosilkovska M, Samer CF, Deglon J, Rebsamen M, Staub C, Dayer P, Walder B, Desmeules JA, Daali Y (2014) Geneva cocktail for cytochrome p450 and P-glycoprotein activity assessment using dried blood spots. Clin Pharmacol Ther 96(3):349–359. https://doi.org/10.1038/clpt.2014.83
Konig J, Muller F, Fromm MF (2013) Transporters and drug-drug interactions: important determinants of drug disposition and effects. Pharmacol Rev 65(3):944–966. https://doi.org/10.1124/pr.113.007518
Kohler GI, Bode-Boger SM, Busse R, Hoopmann M, Welte T, Boger RH (2000) Drug-drug interactions in medical patients: effects of in-hospital treatment and relation to multiple drug use. Int J Clin Pharmacol Ther 38(11):504–513
Chainuvati S, Nafziger AN, Leeder JS, Gaedigk A, Kearns GL, Sellers E, Zhang Y, Kashuba AD, Rowland E, Bertino JS Jr (2003) Combined phenotypic assessment of cytochrome p450 1A2, 2C9, 2C19, 2D6, and 3A, N-acetyltransferase-2, and xanthine oxidase activities with the "Cooperstown 5+1 cocktail". Clin Pharmacol Ther 74(5):437–447. https://doi.org/10.1016/S0009-9236(03)00229-7
Dumond JB, Vourvahis M, Rezk NL, Patterson KB, Tien HC, White N, Jennings SH, Choi SO, Li J, Wagner MJ, La-Beck NM, Drulak M, Sabo JP, Castles MA, Macgregor TR, Kashuba AD (2010) A phenotype-genotype approach to predicting CYP450 and P-glycoprotein drug interactions with the mixed inhibitor/inducer tipranavir/ritonavir. Clin Pharmacol Ther 87(6):735–742. https://doi.org/10.1038/clpt.2009.253
Daali Y, Samer C, Deglon J, Thomas A, Chabert J, Rebsamen M, Staub C, Dayer P, Desmeules J (2012) Oral flurbiprofen metabolic ratio assessment using a single-point dried blood spot. Clin Pharmacol Ther. https://doi.org/10.1038/clpt.2011.247
Bosilkovska M, Samer C, Deglon J, Thomas A, Walder B, Desmeules J, Daali Y (2016) Evaluation of mutual drug-drug interaction within Geneva cocktail for cytochrome P450 phenotyping using innovative dried blood sampling method. Basic Clin Pharmacol Toxicol 119(3):284–290. https://doi.org/10.1111/bcpt.12586
Krumsiek J, Suhre K, Evans AM, Mitchell MW, Mohney RP, Milburn MV, Wagele B, Romisch-Margl W, Illig T, Adamski J, Gieger C, Theis FJ, Kastenmuller G (2012) Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information. PLoS Genet 8(10):e1003005. https://doi.org/10.1371/journal.pgen.1003005
Tay-Sontheimer J, Shireman LM, Beyer RP, Senn T, Witten D, Pearce RE, Gaedigk A, Gana Fomban CL, Lutz JD, Isoherranen N, Thummel KE, Fiehn O, Leeder JS, Lin YS (2014) Detection of an endogenous urinary biomarker associated with CYP2D6 activity using global metabolomics. Pharmacogenomics 15(16):1947–1962. https://doi.org/10.2217/pgs.14.155
Waters MD, Fostel JM (2004) Toxicogenomics and systems toxicology: aims and prospects. Nat Rev Genet 5(12):936–948. https://doi.org/10.1038/nrg1493
Bersanelli M, Mosca E, Remondini D, Giampieri E, Sala C, Castellani G, Milanesi L (2016) Methods for the integration of multi-omics data: mathematical aspects. BMC Bioinformatics 17(Suppl 2):15. https://doi.org/10.1186/s12859-015-0857-9
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Science+Business Media, LLC
About this protocol
Cite this protocol
Ivanisevic, J., Thomas, A. (2018). Metabolomics as a Tool to Understand Pathophysiological Processes. In: Giera, M. (eds) Clinical Metabolomics. Methods in Molecular Biology, vol 1730. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7592-1_1
Download citation
DOI: https://doi.org/10.1007/978-1-4939-7592-1_1
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-7591-4
Online ISBN: 978-1-4939-7592-1
eBook Packages: Springer Protocols